code: def test_model_lightgcn(deeprec_resource_path, deeprec_config_path):
      data_path = os.path.join(deeprec_resource_path, "dkn")
      yaml_file = os.path.join(deeprec_config_path, "lightgcn.yaml")
      user_file = os.path.join(data_path, r"user_embeddings.csv")
      item_file = os.path.join(data_path, r"item_embeddings.csv")
  
      df = movielens.load_pandas_df(size="100k")
      train, test = python_stratified_split(df, ratio=0.75)
  
      data = ImplicitCF(train=train, test=test)
  
      hparams = prepare_hparams(yaml_file, epochs=1)
      model = LightGCN(hparams, data)
  
      assert model.run_eval() is not None
      model.fit()
      assert model.recommend_k_items(test) is not None
      model.infer_embedding(user_file, item_file)
      assert os.path.getsize(user_file) != 0
      assert os.path.getsize(item_file) != 0
  
file_path: recommenders/tests/smoke/recommenders/recommender/test_deeprec_model.py
imports: import os
  import pytest
  from recommenders.datasets import movielens
  from recommenders.datasets.python_splitters import python_stratified_split
label_desc: Use image node_id 6 for calling a global function with example usage: test_model_lightgcn(deeprec_resource_path, deeprec_config_path) without return types